Parameter Identification Algorithm Research Articles

Parameter identification of rock mass in the time domain

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation.

An Identification-Based Control Method for an Overhead Crane with a New Combined Sensor Placement

This paper is devoted to an automatic control method for an overhead crane under the current parametric uncertainty of the crane, transported cargo, and exogenous disturbances. The control objective is to move the cargo in the horizontal plane to a point ensuring the final delivery of the cargo to the designated place while parrying the angular oscillations of the suspension and reaching given dynamic characteristics. The approach is based on a control scheme with a current parametric identification algorithm, an implicit reference model, and “simplified” adaptability conditions to track cargo movements directly. The control law generates a given trolley speed for the servo drive. The passport data of the crane installation are used to select the control law parameters. Unlike previous publications on the topic, the solution proposed below is simpler, more reliable in terms of operation, and less expensive. This is achieved by placing a combined sensor (an accelerometer with an angular rate sensor (ARS)) on a suspension cable near the crane trolley and applying, first, an algorithmic solution without the preliminary calculation of the ARS drift and, second, a current parametric identification procedure of higher efficiency. Computer simulation results are provided to confirm these advantages of the new solution. A similar example is implemented on an experimental installation.

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

Joint Model Parameter Identification and EKF Algorithm for the SOC Estimation of LFP Battery

Abstract Accurately estimating the state of charge (SOC) of batteries is crucial for achieving the safety and efficient driving of electric vehicles. To address the negative impact of voltage platform flatness and accumulated errors in current sampling, the SOC estimation method jointing model parameter identification and extended Kalman filter (EKF) algorithm is proposed and verified through simulation in this paper. Firstly, the parameter identification method is obtained based on the second-order dual polarization model, and effective identification of the parameters under different SOC is achieved using experimental conditions of hybrid pulse power characteristic and constant current discharge. On this basis, a function model with SOC as the independent variable and model parameters as the dependent variable is established by jointing model parameter identification and EKF algorithm, and the iterative estimation of SOC is achieved through the 1stopt and cftool methods. Finally, the SOC estimation accuracy of the proposed method is validated under three operating conditions that adopt the latest standards and are closer to the actual driving environment. The simulation results show that the SOC estimation method jointing model parameter identification and EKF algorithm has higher accuracy and smaller fluctuations than the traditional AH integration method, and the mean squared error (MSE) of estimation for the four test conditions are less than 0.29%, 0.72% and 0.25%, respectively.

Feasibility of ultrasonic non-destructive testing of ancient funerary urns: Experimental and analytical parametric model

Non-invasive modalities are being developed as the computer tools and industrial non-destructive testing capabilities become more widespread. Using the same investigation methods as those applied to modern objects, we seek to study ancient funerary urns containing human bones. The exploration of ancient urns is key to increasing our understanding of the practices of burial and cremation in archaeo-anthropology. But those urns present at archaeological sites are exposed to diversified environments (wet, temperate or dry environment), and can be subjected to chemical or mechanical destructive actions over time. As ancient funeral urns containers are sometimes made of lead, stone or ceramic, the use of X-ray scanners is difficult or even impossible for in situ control. In this work, we propose an engineering technique based on ultrasonic non-destructive testing and wave propagation in urns. An analytical parametric model is developed using mathematical signal processing tools and validated by means of laboratory experiments under controlled conditions of propagation through reproductions of ancient urns for two ultrasonic frequencies (500 kHz and 1 MHz). The aim is to characterise an ancient urn by creating an analytical model, and to use a parametric identification algorithm to determine the presence or absence of bone fragments. The parametric identification algorithm based on the Levenberg–Marquardt method makes it possible to determine the geometrical (thickness) and physical (wave velocity and attenuation, mass density) parameters when the urn is filled with water, with water and bones, and with water, bones and sand. We show that the model makes it readily (processing time ≈15 sec) possible to find the different times of flight between the transducer and the different walls of the urn and the parameters with an accuracy less than 10%.

A modified slime mold algorithm for parameter identification of hydrogen-powered proton exchange membrane fuel cells

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation.

State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence.

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Harmonic voltage measurement based on capacitive equipment dielectric equivalent model and responding current

Abstract With the increasing proportion of new energy and the power electronic equipment in the power grid, accurate measurement of harmonic voltage has become increasingly important for power quality monitoring. In order to solve the problem of high-precision measurement of harmonic voltage in the power grid, this manuscript proposes a high-precision harmonic voltage measurement method based on the dielectric equivalent model (DEM) of capacitive equipment and its responding current. Based on DEM, a voltage-current transfer function of the capacitive device is established, and harmonic voltage is reconstructed with the responding current. Considering the dielectric relaxation characteristics of capacitive device other than a pure capacitor model, this manuscript analyzes the fitting performance of different equivalent capacitance models and improves the traditional pure capacitance model to a more suitable DEM for harmonic voltage reconstruction. The DEM parameters of capacitive devices are obtained through the frequency domain spectroscopy (FDS) and intelligent parameter identification algorithms, which improved the measurement accuracy of harmonic voltage and reduced computational complexity. The harmonic voltage testing platform is established to test the simulated high-voltage harmonics and the harmonic voltage of the actual grid voltage. The results show that the proposed harmonic voltage measurement method can meet the high-precision reconstruction of harmonic voltage in the frequency range of 50~2500Hz, and the system testing error with sensors is less than 2%. The testing accuracy is higher than traditional voltage transformers and testing systems based on pure capacitance models.

Intelligent automatic operational modal analysis

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.

Identification of PV Parameters Based on Bézier Curve and AWSO

AbstractTo achieve accurate PV cell parameter identification, a method combining the second‐order Bézier function with the adaptive war strategy optimization (AWSO) algorithm is proposed, the Bézier curve for fitting the PV output characteristic curve is given by using the linear connection between the filling factor and the control points of the second‐order Bézier function, and then, the Bézier‐AWSO model is constructed, and an adaptive weight‐based updating and allocation method is given to improve the global search capability of the AWSO algorithm in the problem of recognizing five important parameters of photovoltaic, and avoid falling into the local optimum in the process of recognition. Finally, the optimal solution for the unknown parameters in the single diode topology of silicon‐based photovoltaic cells is obtained by using the data points of the fitted curves and the AWSO algorithm, and the accuracy of the AWSO algorithm for photovoltaic cell parameter identification is verified through the analysis of the arithmetic examples and real experiments. The results demonstrate that the parameter identification of the proposed improved model reduces the average relative error by 0.5%–1% compared with that before the improvement under standard and non‐standard test conditions, which improves the accuracy of the 5‐parameter identification results of the silicon‐based PV cell and provides a reference for the subsequent fault analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Parameter Identification of Maritime Vessel Rudder PMSM Based on Extended Kalman Particle Filter Algorithm

To address the issue of system parameter variations during the operation of a maritime light vessel rudder permanent magnet synchronous motor (PMSM), an extended Kalman particle filter (EKPF) algorithm that combines a particle filter (PF) with an extended Kalman filter (EKF) is proposed in this paper. This approach enables the online identification of motor resistance and inductance. For highly nonlinear problems that are challenging for traditional methods such as Kalman filtering, this algorithm is typically a statistical and effective estimation method that usually yields good results. Firstly, a standard linear discrete parameter identification model is established for a PMSM. Secondly, the PF algorithm based on Bayesian state estimation as a foundation for subsequent research is derived. Thirdly, the advantages and limitations of the PF algorithm are analyzed, addressing issues such as sample degeneracy, by integrating it with the Kalman filtering algorithm. Specifically, the EKPF algorithm for online parameter identification is employed. Finally, the identification model within MATLAB/Simulink is constructed and the simulation studies are executed to ascertain the viability of our suggested algorithm. The outcomes from these simulations indicate that the proposed EKPF algorithm identifies resistance and inductance values both swiftly and precisely, markedly boosting the robustness and enhancing the control efficacy of the PMSM.

State of Charge Estimation of Lithium-ion Batteries Based on Online OCV Curve Construction

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method’s ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions.

A faulty feeder selection method for SLG faults based on active injection approach in non-effectively grounded DC distribution networks

Identifying single-line-to-ground faults in non-effectively grounded DC distribution networks is challenging because the fault current is fleeting in the transient stage and negligible in the fault steady stage. First, an active signal injection method is proposed to inject signals into the DC network after the fault occurs. And the fault characteristics of the zero-mode fault circuit including the injected signal are analyzed. Second, a zero-mode parameter identification algorithm is proposed to calculate the length of each feeder based on the injected signal in the fault steady stage. It is found that the calculated length of the healthy feeder corresponds to its actual length, but the calculated result of the faulty feeder is the opposite of the total length of all healthy feeders. Finally, a faulty feeder selection method based on the polarity of the calculated length is proposed. The simulation results shown that the method can select faulty feeders under conditions of up to 5000 Ω transient resistance and 20 dB noise.

Parameters identification and application of composite plate reinforcement mesh based on DXF data

Abstract This paper presents an algorithm for identifying the process parameters of a laminated plate reinforcement mesh. To accommodate the actual production needs, the computer software of the production line is employed for the identification of parameters and data processing of the DXF steel mesh file. In this context, a C++ programming algorithm is utilized for the identification of the parameters of the imported DXF file, as per the production line’s processing requirements. DXF is an AutoCAD vector graphics exchange file that is available in two storage formats: ASCII format and binary format. The ASCII format is easily readable and therefore, DXF is widely used, with most CAD software capable of reading or outputting DXF files. This paper initially scrutinizes the structural attributes of DXF files, normalizes the design standard of graphics files, and then reads the coordinates of each point of the POLYLINE entity. A parameter identification algorithm is devised to address the issue of steel bar bending in drawings, with the program designed to verify its accuracy.

Revolutionizing motor dysfunction treatment: A novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

An Operational Modal Parameters Identification Algorithm for Structures Based on HHT and RDT

An Operational Modal Parameters Identification Algorithm for Structures Based on HHT and RDT

Dynamic Characterization of SynRM With Dual-Axis Hybrid Excitation Self-Commissioning

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.